Somrat390/Algerian-Forest-Weather-Index-prediction
Domain:
environment and energyclimateRecord type:
project
Creator:
Som
Host:
# 🌲 Algerian Forest Fire Weather Index Prediction
This project predicts the **Fire Weather Index (FWI)** for the Algerian Forest dataset using a **Ridge Regression Model**.
It is deployed on **Render** as a Flask web application.
---
## 📊 Dataset
- **Source:** Kaggle - Algerian Forest Fires Dataset
- **Features include:**
- 🌡️ Temperature (°C)
- 💧 Relative Humidity (%)
- 🌬️ Wind Speed (km/h)
- 🌧️ Rain (mm)
- 🔥 Fire Weather Codes → FFMC, DMC, ISI
- 🏷️ Classes (0 = No Fire, 1 = Fire)
- 🗺️ Region (Bejaia / Sidi-Bel Abbes)
---
## ⚙️ Tech Stack
- **Python 3**
- **Flask** (Web Framework)
- **Scikit-Learn** (ML Model)
- **NumPy, Pandas** (Data Processing)
- **Render** (Deployment)
---
## 🚀 Deployment
The project is deployed on **Render** and can be accessed here:
🔗 **Live Demo:** algerian-forest-weather-ind…
---
## 🖥️ Local Setup
Follow these steps to run the project locally:
### 1️⃣ Clone the Repository
```bash
git clone github.com
cd Algerian-Forest-Weather-Index-prediction
````
### 2️⃣ Create Virtual Environment & Install Dependencies
```bash
python -m venv venv
source venv/bin/activate # For Mac/Linux
venv\Scripts\activate # For Windows
pip install -r requirements.txt
```
### 3️⃣ Run the Flask App
```bash
python application.py
```
Your app will start at **127.0.0.1
---
## 📌 Example Prediction
**Input Example:**
| Feature | Value |
| ----------- | ---------- |
| Temperature | 25°C |
| RH | 40% |
| Ws | 15 km/h |
| Rain | 0 mm |
| FFMC | 85 |
| DMC | 120 |
| ISI | 10 |
| Classes | 1 (Fire) |
| Region | 1 (Bejaia) |
**Output Example:**
🔥 **Predicted Fire Weather Index:** `42.31`
---
## 📂 Project Structure
```
├── Model/
│ ├── ridge.pkl
│ ├── scaler.pkl
├── templates/
│ ├── index.html
│ ├── home.ht …